The method is likely to make laboratory studies of cancers and other human diseases, and assessment of new drugs to target them, more accurate.
The study was completed by Dr Rune Linding, head of the Cellular and Molecular Logic Team at The Institute of Cancer Research (ICR) in the UK, along with UK and Canadian-based colleagues. The research is published in the latest edition of the journal Science.
Dr. Linding says that understanding communication between cells is crucial, as many cancers and other diseases are caused by a breakdown in communications systems.
“Organs and tissues are composed of many different cell types with distinct roles to play,” Dr Linding says. “To function properly, the cells must communicate with each other, which they do through a network of specialised proteins known as signalling molecules. When cells are unable to send or receive the correct signals, they can behave abnormally and this can lead to disease.”
Until now, scientists have generally studied cell communication by taking a single population of cells, adding a molecule to stimulate the cells and measuring the level of signalling molecules produced. But this technique does not take into account that cells respond to the signals they receive and feedback to each other, like a conversation between people.
“In our latest study, we have developed a way to more accurately replicate what’s happening in the body – before scientists could only hear a monologue, but now for the first time we can assess the outcome of a conversation,” Dr Linding says.
The new method involves growing cells in media containing labelled amino acids (the fundamental building blocks of proteins) that are incorporated into the cells’ proteins. Two cell types, grown with different labels, are then combined for a short time to allow them to talk to each other, and then the cells are broken open so the proteins produced can be examined. A technique called mass spectrometry is then used to measure the level of each label, showing from which cell type the proteins originated.
The team then looked for genes that were involved in the conversation. They tested about 10 per cent of all human genes by blocking them within the cells one by one, using small interfering RNA molecules, and measured whether the cells behaved differently. Information about the proteins and genes was used to make a computer model of the signalling networks involved – effectively highlighting the important points in the conversation.